A computed tomography (CT) imaging system typically includes an x-ray source that projects fan- or cone-shaped x-ray beams through an object being imaged, such as a patient, to an array of radiation detectors. The beam is collimated to lie within an X-Y plane, or a set of such planes generally referred to as the “imaging planes.” Intensity of radiation from the beam received at the detector array depends on attenuation of the x-ray beam by the object. Attenuation measurements from each detector are acquired separately to produce a transmission profile.
The x-ray source and the detector array are rotated within a gantry and around the object to be imaged so that a projection angle at which the x-ray beam intersects the object constantly changes. A group of x-ray attenuation measurements (such as integral projection data from the detector array at one gantry angle) is referred to as a “view”. A “scan” of the object comprises a set of views made at varying projection angles, during one revolution of the x-ray source and detector array.
In an axial scan, the projection data is processed to construct an image that corresponds to one or more two-dimensional slices taken through the object. To form these slices, iterative reconstruction of a full field of view may be performed to increase image quality. Iterative reconstruction refers to a method that forms an image by repeatedly adjusting an existing estimate according to the quality of a match between measured data and simulated measurements from a current estimate of the image. The quality of the match may also be affected by consideration of the characteristics of the image alone, such as its smoothness and/or satisfaction of a pre-established model. Multiple iterations are performed to create a resulting reconstructed image that approximately matches the acquired projection data. A full set of reconstructed images is referred to as a 3-D reconstruction, since the set is formed into a three dimensional representation of the object with each image pixel or picture element corresponding to a single voxel or volume element in the 3-D reconstruction.
To reduce the total scan time required for multiple slices, a “helical” scan may be performed. Helical scan techniques allow for large volumes of the object to be scanned at a quicker rate using one or more photon sources. To perform a “helical” scan, the patient is moved along the z-axis, the axis about which the gantry rotates, synchronously with the rotation of the gantry, while data for a prescribed number of slices are acquired. Such a system generates a single helix from a fan beam helical scan. The helix mapped out by the fan beam yields projection data from which images in each prescribed slice may be reconstructed. In addition to reducing scan time, helical scanning provides other advantages such as better use of injected contrast, improved image reconstruction at arbitrary locations, and better three-dimensional images.
Traditionally, direct analytical algorithms, such as the Filtered Back-Projection (FBP) algorithm, have been used to reconstruct images from CT data. Iterative techniques, such as the Maximum A Posteriori Iterative Coordinate Descent (MAP-ICD) algorithm, have also been recently considered for reconstruction of volumetric CT data to provide means to improve general image quality over conventional techniques. It has been demonstrated that reduced noise, enhanced resolution, better low contrast performance, and reduced artifacts, can all be achieved with iterative reconstruction of clinical images. One important family of iterative algorithms works by optimizing a cost function formed of a data fit term and a penalization term. The data fit term describes a model wherein synthesized projections from an image estimate must match the acquired projection measurements, and may include a statistical weighting to apply different degrees of confidence to each datum depending on its noise characteristics. The penalization term typically enforces a smoothness constraint on the reconstructed images, and may treat differently homogeneous regions and regions with a large local gradient such as edges and organ boundaries. An iterative algorithm is applied to iteratively refine an image estimate from a set of initial conditions so as to minimize the resulting global cost function. When the minimum of the cost function has been achieved, the iterative algorithm has converged to the solution. For multi-slice CT data, the solution is a three-dimensional volume of image estimates that best matches the acquired data based on the model described in the cost function.
Several algorithms have been developed for iterative optimization of the cost function, such as the Ordered Subset (OS) algorithm and the Iterative Coordinate Descent (ICD) algorithm. These techniques use different iterative numerical approaches to converge to a solution that has improved image quality. While these techniques have provided large advances in diagnostic capability, it has come at the cost of greatly increased computation time to reconstruct the images when compared with traditional single-pass methods such as FBP. The amount of time the iterative optimization techniques takes to converge to a solution depends on the characteristics of the data and the initial conditions. Some of the techniques, such as OS for example, converge quickly on data sets that are more homogeneous or uniform, commonly referred to as “low frequency” data. Other techniques, such as ICD for example, converge quickly on data sets with edges and noise, commonly referred to as “high frequency”, data but require more computation time for homogeneous regions. When a given technique encounters a data type that it is less efficient, the number of iterations and hence computation time, increases.
Further, in the clinical environment, images are reconstructed to zoom over the portion of the anatomy relevant for diagnosis. To reconstruct a targeted area, iterative reconstruction algorithms differ from conventional techniques such as FBP in that they generally require reconstructing the entire field of view, which includes all the objects measured by the CT system. Such a full field reconstruction is performed with iterative reconstruction algorithms to achieve good image quality. This is due to the fact that iterative reconstruction requires the consideration of all possible sources of x-ray attenuation along the whole path lengths between the x-ray source and the detector. However, this implies significant computational cost for targeted reconstruction of a small area. For instance, reconstructing a 512×512 image in 35 cm field of view where the bore of the CT scanner is 70 cm in diameter would require iterating over a 1024×1024 image in 70 cm field of view (assuming uniform pixel spacing) to guarantee that all possible sources of x-ray attenuation are captured in the reconstruction. That would be four times the number of voxels reconstructed with FBP.
While existing reconstruction techniques are suitable for their intended purpose, there is a need for improvements, especially in reducing the amount of time needed to reconstruct an image while maintaining high quality levels provided by iterative optimization techniques.